Research Article Automatic Eye Winks Interpretation System for

Hindawi Publishing Corporation
EURASIP Journal on Image and Video Processing
Volume 2007, Article ID 65184, 9 pages
doi:10.1155/2007/65184
Research Article
Automatic Eye Winks Interpretation System for
Human-Machine Interface
Che Wei-Gang,1 Chung-Lin Huang,1, 2 and Wen-Liang Hwang3
1 Department
of Electrical Engineering , National Tsing-Hua University, Hsin-Chu, Taiwan
of Informatics, Fo-Guang University, I-Lan, Taiwan
3 Institute of Information Science, Academic Sinica, Taipei, Taiwan
2 Department
Received 2 January 2007; Revised 30 April 2007; Accepted 21 August 2007
Recommended by Alice Caplier
This paper proposes an automatic eye-wink interpretation system for human-machine interface to benefit the severely handicapped people. Our system consists of (1) applying the support vector machine (SVM) to detect the eyes, (2) using the template
matching algorithm to track the eyes, (3) using SVM classifier to verify the open or closed eyes and convert the eye winks into a
sequence of codes (0 or 1), and (4) applying the dynamic programming to translate the code sequence to a certain valid command.
Different from the previous eye-gaze tracking methods, our system identifies the open or closed eye, and then interprets the eye
winking as certain commands for human-machine interface. In the experiments, our system demonstrates better performance as
well as higher accuracy.
Copyright © 2007 Che Wei-Gang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1.
INTRODUCTION
Recently, there has been an emerging community of machine perception scientists focusing on automatic eye detection and tracking research. It can be applied for vision-based
human-machine interface (HMI) applications such as monitoring human vigilance [1–6] and assisting the disable [7, 8].
The eye detection and tracking approaches can be classified
into two categories: CCD camera-based approaches [1–11]
and active IR-based approaches [12–15].
An eye-wink control interface [7] is proposed to provide
the severely disabled with increased flexibility and comfort.
The eye tracker [2] makes use of a binary classifier with a dynamic training strategy and an unsupervised clustering stage
to efficiently track the pupil (eyeball) in real time. Based on
optical flow and color predicates, the eye tracking [4] can robustly track a person’s head and facial features. It classifies
the rotation of all viewing directions, detects eye blinking,
and recovers the 3D gaze of the eyes. In [5], the eye detection operates on the entire image, looking for regions that
have the edges with a geometrical configuration similar to
the expected one of the iris. It uses the mean absolute error
measurement for eye tracking and a neural network for eyes
validation.
The eye movement collection data can be analyzed to determine the pattern and duration of eye fixations and the se-
quence of scan path as a user visually moves his eyes. The
eye tracking researches, such as the eye-gaze estimation [2, 9]
and eye blink rate analysis [3–5], can be applied to analyze
the fatigue and deceit of human being [6]. Another application such as head-mounted goggle type eye detection device
[10] with a liquid crystal display is developed for investigation of eye movements of neurological disease patients. In
[11], they formulate a probabilistic image model and derive
optimal inference algorithms for finding objects. It requires
the likelihood-ratio models for object versus background,
which can be applied to find faces and eyes on arbitrary images.
The active approaches [12–15] make use of IR devices for
the purposes of pupil tracking based on the special bright
pupil effect. This is a simple and very accurate approach
to pupil detection using the differential infrared lighting
scheme. By combining imaging by using IR light and object recognition techniques, the method proposed in [14]
can robustly track eyes even when the pupils are not very
bright due to significant external illumination interference.
The eye detection and tracking process is based on the support vector machine (SVM) and mean shift tracking. Another method [15] exhibits robustness to light changes and
camera defocusing. It is capable of handling sudden changes
between IR and non-IR light conditions, without changing
parameters.
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EURASIP Journal on Image and Video Processing
Locating face in the next
frame
No
Face found
Yes
Eye detection using SVM
No
Update eye template
Eye found
Yes
Tracking eye using template
matching
No
Identify open or closed eye
Command interpreter using
dynamic programming
Eye matched
Yes
Figure 1: Flowchart of the eye-winks interpretation system.
However, the active methods [12–15] require additional
resources in the form of infrared sources and infrared sensors. Here, we propose a vision-based eye-wink control interface for helping the severely handicapped people to manipulate the household devices. Recently, many researchers have
shown great interests in the research topics of HMI. In this
paper, we propose an eye-wink control HMI system which
allows the severely handicapped people to control the appliances by using their eye winks. We assume that the possible head poses of the handicapped people are very limited.
Under the front-pose assumption, we may easily locate the
eyes, track the eyes, and then identify the open or closed
eye.
Before eye tracking, we use the skin color information to
find the possible face region which is a convex region larger
than a certain size. Once the face region is found, we define
the possible eye region in which we may apply SVM to localize the eyes precisely and create the eye template obtained
from the identified eye image. In the next frame, we apply the
template matching to track the eyes based on the eye template
extracted in the previous frame. The eye template is updated
every time the eye is successfully tracked. After eye tracking,
we apply SVM to verify whether the tracked block is an eye
or a noneye region. If it is an eye region, then we use SVM
again to identify whether it is an open eye or a closed eye,
and then convert the eye winks to a sequence of 1 or 0 codes.
Finally, we apply the dynamic programming to validate the
code sequence and convert the code sequence into a certain
command. The flow chart of the proposed system is illustrated in Figure 1.
2.
EYE DETECTION AND TRACKING
Our eye detection and tracking method consists of three
stages: (1) face region detection, (2) eye localization, and (3)
eye tracking. The three steps are illustrated in the following
sections.
2.1.
Face region detection
To reduce the eye search region, we need to locate the possible face region. In the captured human face images, we assume that the color distribution of the human face is somehow different from that of the image background. Pixels belonging to face region exhibit similar chrominance values
within and across people of different ethnic groups [16].
However, the color of face region may be affected by different
illuminations in the indoor environment. For skin color detection, we analyze the color of the pixels in HSI color space
to decrease the effect of illumination changes, and then classify the pixels into face color or nonface color based on their
hue component only.
Similar to [17], we analyze the statistics of skin color and
nonskin color distributions from a set of training data to
obtain the conditional probability density functions of skin
color, and nonskin color. From 100 training close-up face images, we have the probability density function of hue value
H, which can be either face color and nonface color (i.e.,
p(H—face) and p(H—nonface)). Based on hue statistics, we
use the Bayesian approach to determine the face color region. Each pixel is assigned to the face or nonface class that
gives the minimal cost when considering cost weightings on
the classification decisions. The classification is performed by
using the Bayesian decision rule which can be expressed as: if
p(H—face)/p(H—nonface) > τ, then the pixel (with H hue
value) belongs to a face region, otherwise it is inside a nonface region, where τ = p(nonface)/p(face). After applying the
Bayesian classification on another 100 testing close-up face
images, we find that the hue values of 99% of the correct classified facial pixels are within the range [0.175, 0.285].
Che Wei-Gang et al.
3
After the face pixel classification, we cluster these pixels into face color regions. A merging stage is then iteratively performed on the set of homogeneous face color pixels to provide a contiguous region as the candidate face area.
Constraints of shape and size of face region are applied on
each candidate face area for potential face region detection.
Figure 2(a) is the original face image, and Figure 2(b) illustrates the corresponding hue distribution in a 3D coordinate.
The face color is distributed in a specified range (in blue),
and the z-axis shows the normalized hue value (between 0
and 1). To determine the face region, we perform the vertical and horizontal projections on the classified face pixels and
find the right and left region boundaries where the projecting
value exceeds a certain threshold. The extracted face region is
shown in Figure 2(c). We use 100 images of different subjects,
background complexities, and lighting conditions to test our
face detection algorithm. The correct face detection rate is
88%. The system does not know whether the identified face
region is accurate or not, however, if the following eye detection can not locate an eye region, then the detected face
region is not a false alarm.
After the face region detection, there may be more than
one face-like region in the block image. We select the maximum region as the face region. We assume that eyes should
be located in the upper half face area. Once the face region
is found, we may assume that the possible eye region is the
upper portion of the face region (i.e., the yellow rectangle
as shown in Figure 2(d)). These eyes are searched within the
yellow rectangle area only.
2.2. Eye localization using SVM
The support vector machine (SVM) [18] is a general classification scheme that has been successfully applied to find
a separating hyperplane by maximizing the margin between
two classes, where the margin is defined as the distance of the
closest point in each class to the separating hyperplane. Given
a data set {xi }Ni=1 of examples with labels yi ∈ {−1, +1}, we
find the optimal hyperplane by solving a constrained optimization problem using quadratic programming, where the
optimization criterion is the width of the margin between
the classes. The separating hyperplane can be represented as
a linear combination of the training examples and classifying
a new test pattern x by using the following expression:
f (x) =
N
i=1
αi yi k(x, xi ) + b,
(1)
where k(x, xi ) is a kernel function and the sign of f (x) determines the class membership of x. Constructing the optimal
hyperplane is equivalent to finding the nonzero αi . Any data
point xi corresponding to nonzero αi is termed “support vector.” Support vectors are the training patterns closest to the
separating hyperplane. A training process is developed to determine the optimal hyperplane of the SVM.
The efficiency of SVM classification is based on the selected features. Here, we convert the eye edge image block
as the feature vector. An eye edge image is represented by a
feature vector consisting of the edged pixel values. We man-
ually select the two classes: positive set (eye) and negative set
(noneye). The eye images are processed by using histogram
equalization and their image sizes are normalized to 20 × 10.
Figure 3 shows the training samples consisting of open eye
images, closed eye images, and noneye images.
Supervise learning algorithms (such as SVM) require as
many training samples as possible to reach higher accuracy
rate. Since we do not have so many training samples, the alternative way is to retrain the classifier by reusing the missclassified testing samples as the training samples. Here, we
have tested at least one thousand unlabeled samples, and
then we select the misslabeled data for retraining the classifier. After applying this retraining process, based on the misslabeled data, several times, we can boost the accuracy of the
SVM machine.
The eye detection algorithm will search every candidate
image block inside the possible eye region to locate the eyes.
Each image block is processed by Sobel edge detector and
converted to a feature vector consisting of edge pixels, which
is more insensitive to the intensity change. With the feature
vector (with dimension 200), the image block will be classified by the SVM as an eye block or a noneye block.
2.3.
3 Eye tracking
Eye tracking is applied to find the eye in each frame by using
template matching. Given the detected eyes in the previous
frame, the eyes in subsequent frames can be tracked frame
by frame. Once the eye is correctly localized, we update the
eye templates (gray level image) for eye tracking in the next
frame. The search region in the next frame is defined by extending 50% length in four directions of the previously located eye bounding box. We individually normalize an eye
template as a 20 × 10 block so that we may track different size
eye images which are normalized for template matching. We
consider an eye template t(x, y) located at (a, b) of the image
frame f (x, y). To compute the similarity between the candidate image blocks and the eye template, we have the following
equation as
M(p, q) = min
h
w x=0 y =0
| fn (x + p, y + q) − tn (x, y)| ,
(2)
where (1) w and h are the width and height of the eye template tn (x, y) and (2) p and q are offsets of the x-axis and
y-axis, that is, a − 0.5∗ w < p < a + 0.5∗ w and b − 0.5∗ h <
q < b + 0.5∗ h. If M(p , q ) is the minimum value within the
search area, the point (p , q ) is defined as the best matched
position of the eye, and (a, b) is updated by the new position
(p , q ) as (a, b) = (p , q ). The new eye template is applied
for the eye tracking in the next frame.
People sometimes blink their eyes unintentionally that
may cause error propagation in the template matching process and make the eye tracking fail. Here, we estimate the
centroid of the eye in the following frame for the template
matching process. To find the centroid, we apply Otsu algorithm [18] to convert the tracked eye image to a binarized
image. In the open eye image, the pupil and iris pixels (which
are darker) can be segmented as shown in Figure 4(b). The
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EURASIP Journal on Image and Video Processing
0.5
0
0
20
40
60
0
80
50
100
120
(a)
(c)
100
150
(b)
(d)
Figure 2: Results of face detection. (a) The original image. (b) Hue distribution of the original image. (c) Skin color projection. (d) Possible
eye region.
centroid of iris and pupil is the centroid of the eye region
which is located at the center of bounding box. However, in
the closed eye image, the centroid is located at the center of
eyelashes instead of the center of bounding box. Based on
the centroids of the binarized images, the eye tracking will
be faster and more accurate. Once the eye region is tracked,
we apply the SVM again to classify the tracked region as an
open eye, a closed eye, or a noneye region. If the tracked image is a non-eye region, the system will restart the face and
eye localization procedures.
3.
THE COMMAND INTERPRETER USING
DYNAMIC PROGRAMMING
After eye tracking, we continue using SVM to distinguish between the open eye and the closed eye. If the eye opens and
exceeds a fixed duration, then it represents a digit “1”. Sim-
ilarly, the closed eye represents a digit “0”. So we can convert the sequence of eye winks to a sequence of 0 and 1. The
command interpreter validates the sequence of codes, and issues the corresponding output command. Each command is
represented by the corresponding sequence of codes. Starting from the base state, the user issues a command by a sequence of eye winks. The base state is defined as an open eye
for a long time without intentionally closing the eye. The input sequence of codes is then matched with the predefined
sequence of codes by the command interpreter.
To avoid an unintentional or very short eye wink, we require that the duration of a valid open or closed eye should
exceed a duration threshold θ tl . If the time interval of the
continuously open or closed eye is longer than θ tl , then it
can be converted to a valid code, that is, “1” or “0”. However, we may allow two contiguous “1” or “0”, so we define another threshold θ th ≈ 2θ tl . If the time interval of the
Che Wei-Gang et al.
5
(a)
(b)
(c)
Figure 3: (a) The open eye images. (b) The closed eye images. (c) The noneye images.
(a)
(b)
(c)
(d)
Figure 4: . (a) The original open eye. (b) The binarized open eye. (c) The original closed eye. (d) The binarized closed eye.
continuously open or closed eye is longer than θ th , then we
may consider it as code 00 or 11. The threshold θ tl is userdependent, user may select the best suitable threshold for his
specific eye blinking condition.
Here, we may predefine some valid code sequences, and
each one corresponds to a specific command. Once the code
sequence has been issued, we need to validate the code sequence. To find a valid code sequence, we need to calculate the similarity (or alignment) score between the issued
code sequence and the predefined code sequences. Because
the code lengths are different, we need to align the two
code sequences to maximize the similarity by using the dynamic programming [19]. We assume the predefined codes
as shown in Table 1.
A dynamic programming algorithm consists of four
parts: (1) a recursive definition of the optimal score, (2)
a dynamic programming matrix for remembering optimal
scores, (3) a bottom-up approach of filling the matrix, and
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EURASIP Journal on Image and Video Processing
j
1
0
—
—
—
3
010
—
—
—
4
0010
0100
0110
—
5
00100
00110
01010
01100
(4) a trace back of the matrix to recover the structure of the
optimal solution that generates the optimal score. These four
steps are explained as follows.
Sequence x
Code length
—
—
—
—
i
Table 1: Code sequences.
Sequence y
0
0
1
0
1
0
−2
−4
−6
−8
−10
0
−2
1
−1
−3
−5
−7
0
−4
−1
0
0
−2
−4
1
−6
−3
0
−1
1
−1
0
−8
−5
−2
1
−2
2
Optimum alignment score 2:
3.1. Recursive definition of the optimal
alignment score
There are only three conditions that the alignment can possibly be: (i) residues xM and yN are aligned with each other; (ii)
residue xM is aligned to a gap character and yN appears somewhere earlier in the alignment; or (iii) residue yN is aligned to
a gap character and xM appears earlier in the alignment. The
optimal alignment will be the most preferred of these three
cases. The optimal alignment score of the prefix of sequence
{xl , . . . , xM } to the prefix of sequence { yl , . . . , yN } is defined
as
⎧
⎪
⎪
⎨S(i − 1, j − 1) + σ(xi , y j ),
S(i, j) = max ⎪S(i − 1, j) + γ,
⎪
⎩S(i, j − 1) + γ,
(3)
where i ≤ M and j ≤ N Case (i) is the score σ(xM , yN ) for
aligning xM to yN plus the score S(M −1, N −1) for an optimal
alignment of everything else up to this point. Case (ii) is the
gap penalty γ plus the score S(M − 1, N). Case (iii) is the gap
penalty γ plus the score S(M, N − 1). The divide-and-conquer
approach breaks the problem into independently optimized
pieces, as the scoring system is strictly local to one aligned
column at a time. For instance, the optimal alignment of
{x1 , . . . , xM −1 } to { y1 , . . . , yN −1 } is unaffected by adding the
aligned residue pair xM and yN . The initial score S(0, 0) for
aligning nothing to nothing is zero.
3.2. The dynamic programming matrix
For the pairwise sequence alignment algorithm, the optimal
scores S(i, j) are tabulated in a two-dimensional matrix, with
i = 0 . . . M and j = 0 . . . N, as shown in Figure 5. As we
calculate the solutions to subproblems S(i, j), their optimal
alignment scores are stored in the appropriate (i, j) cell of
the matrix.
3.3. A bottom-up calculation to get the optimal score
the dynamic programming matrix S(i, j) is laid out, it is easy
to fill it in a bottom-up way, from the smallest problems to
progressively bigger problems. We know the boundary conditions in the leftmost column and the topmost row (i.e.,
S(0, 0) = 0; S(i, 0) = γ∗ i; and S(0, j) = γ∗ j). For example,
the optimum alignment of the first i residues of sequence x to
0
1
0
1
0
0
−
0
1
0
+1 −2 +1 +1
+1
Figure 5: An example of the dynamic programming matrix.
Table 2: Result of eye tracking.
Video 1 Video 2
Total frame #
1763
1544
Tracking failure frame # 17
19
Correct rate
99 %
98.7 %
Average correct rate
Video 3 Video 4
583
1241
6
15
98.9 %
98.7 %
98.8 %
nothing in sequence y has only one possible solution which
is to align to the gap characters and pay i gap penalties. Once
we have initialized the top row and left column, we can fill
in the rest of the matrix by using the recursive definition of
S(i, j). So we may calculate any cell based on the three adjoining cells to the upper left (i − l, j − l), above (i − l, j), and to
the left (i, j − l) which are already known. We may iterate two
nested loops, i = l . . . M and j = l . . . N, to fill in the matrix
left to right and top to bottom.
3.4.
A trace back to get the optimal alignment
Once we have finished filling the matrix, the score of the optimal alignment of the complete sequences is the last score,
that is, S(M, N). We still do not know the optimal alignment;
however, we recover this by a recursive trace back of the matrix. Starting from cell (M, N), we determine which of the
three cases (i.e., (3)) can be applied to record that choice as
part of the alignment, and then follow the appropriate path
for that case back into the previous cell on the optimum path.
We keep doing that, one cell in the optimal path at a time,
until we reach cell (0, 0), at which point, the optimal alignment is fully reconstructed. Figure 5 shows an example of
the dynamic programming matrix for two code sequences,
x = 0010 and y = 01010 (0: closed eye, 1: open eye). The
scores of match, mismatch, and insertion or deletion are +1,
−1, and −2, respectively. The optimum path consists of the
cells marked by red rectangles.
Che Wei-Gang et al.
7
(a)
(b)
Figure 6: (a) Open eyes, and (b) closed eyes detected correctly.
Table 3: Results of eye signal detection.
Command 1
Command 2
Command 3
Command 4
Command 5
Command 6
Command 7
Command 8
Command 9
Correct rate
4.
User #1
29/30
15/15
13/13
14/15
13/15
17/17
17/19
18/21
16/17
95 %
Average correct rate
EXPERIMENT RESULTS
We use a Logitech QuickCam Pro3000 camera to capture the
video sequence of the disable, and the image resolution is
320 × 240. The system is implemented on a PC with Athlon
3.0 GHz CPU with Microsoft Windows XP. The eye-wink
control system can achieve the speed of 13 frames per second. We have tested 5131 frames of the videos of four people
under normal indoor lighting conditions. Based on the SVM
for eye detection, the accuracy of the eye detection inside the
correctly detected face region is above 90%. The correct classification rate of the open and closed eye is also higher than
92%. Figure 6 shows that the SVM-based eye classifier can
correctly identify the open and closed eyes. The SVM classifier works fine under different illumination conditions due
to the intensity normalization of the training images via histogram equalization. The face region is separated into the
two parts for the detection of the individual left eye and the
right eye.
Table 2 lists the results of eye tracking applied on four different test videos. “Total frames #” indicates the total number of frames in each video. “Tracking failure frame #” is the
number of frames in which the eye tracking fails. The eye
tracking fails when the system can not locate the eye accu-
User #2
25/27
13/13
15/18
10/12
16/17
14/19
17/19
18/21
8/10
90.7%
User #3
18/18
5/7
12/13
6/7
10/11
8/8
10/12
13/13
6/8
90.6%
User #4
35/38
15/17
18/20
14/17
18/20
6/8
10/12
21/25
17/18
88%
90.1 %
rately, and then it may missidentify the open eye as a closed
eye or vice versa. The correct rate of eye tracking is defined
as
correct rate =
Total frame # − Tracking failure frame
(4)
Total frame #
Table 2 shows that the proposed system achieves 98% correct
eye identification.
Table 3 shows the results of eye-winks interpretation system operating on the test video sequences of four different
users. Our system can interpret nine different commands.
Each command is composed of a sequence of eye winks. For
each command, the correct interpretation rate for a different
user is described in terms of r1 /r2 , where r2 is the total test
video sequences for the specific command issued by the designated user, and r1 indicates the correctly identified video
sequences.
Figure 7 shows our system eye-wink control interface.
The red solid circle indicates that the eyes are open. Similarly,
the green solid circle indicates that the eyes are closed. There
are nine blocks at the right portion. In each block, there is
a binary digit number representing the specific command
code. In the base mode, we design eight categories: medical treatments, a diet, a TV, a radio, anair conditioner, a fan,
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EURASIP Journal on Image and Video Processing
[5]
[6]
(a)
[7]
[8]
[9]
(b)
Figure 7: Program interface. (a) Layer 1 commands. (b) Layer 2
commands for audio.
[10]
a lamp, and a telephone. There are two layers in the command mode, so we can create at most 9∗ 9(81) commands.
Here, we only use 8∗ 8 + 1(65) commands because in each
layer, we have a “Return” command. In Figure 7, we illustrate
layer 1 commands and layer 2 commands.
5.
[11]
CONCLUSION AND FUTURE WORK
We propose an effective algorithm for eye-wink interpretation for human-machine interface. By integrating SVM and
dynamic programming, the eye-wink control interpretation
system will enable the severely handicapped people to manipulate the household appliances by using a sequence of eye
winks. Experimental results have illustrated the encouraging
performance of the current methods in both accuracy and
speed.
[12]
[13]
[14]
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